Digitally Tracking Consumer Engagement
What leads a patient to choose one health system over another?
WebMD reaches about one in four US adults every month, mostly through organic search from people looking into a condition, a treatment, or their options. Luth Research partnered with WebMD to find out exactly where WebMD fits into the patient journey relative to search and social media, using real behavioral data instead of the usual assumptions about attribution.
A Typical Path to Purchase Study Doesn't Work for Healthcare
Most path to purchase research follows a simple pattern: a shopper visits a retailer, browses, and either buys or drops off. Healthcare doesn't work that way. Choosing a health system can take months, involve multiple touchpoints, and never resolve into a single moment of obvious conversion. WebMD needed a way to show its unique contribution inside that longer, messier journey, and a standard framework wasn't built for it.
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WebMD's Reach and the Study Behind It
WebMD reaches roughly one in four US adults each month, largely through organic search
Luth's ZQ Intelligence panel captures behavior across PC, smartphone, and tablet, retaining 18 months of data from a global panel of about 3.5 million people
The analysis covered a defined universe of 50-plus health system websites and traced six months of behavior leading up to each patient's first visit
Redefining What Counts as a Conversion
For a typical retail study, conversion means someone bought the product. For this one, conversion became a patient's first visit to a health system site without a login, used as the anchor point to trace back six months of behavior. From there, Luth and WebMD built a whitelist of medical search terms covering conditions, procedures, and doctor names, then coded when those terms showed up during that six-month window.
What emerged was a clear pattern. WebMD carried the load early, when people were researching surgical options and looking for the best-in-class facilities. Search activity skewed more specific later, once patients were typing in exact doctor or facility names. Social media, when it came into the picture, showed up mostly around community and peer support. Each platform played a distinct role, and none of them told the whole story alone.
Timing, Order, and Overlap
Attribution across a months-long decision is notoriously hard to pin down, and healthcare makes it harder since there's rarely a single moment you can point to as the payoff. By anchoring the analysis to a real, timestamped first visit and tracing the behavior that led there, Luth and WebMD could show not just that these platforms mattered, but when, in what order, and how much each one overlapped with the others.
Apply This to Your Own Long-Consideration Category
A time-based, behavior-first framework can clarify attribution in any category where decisions take months and cross many channels.
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